An enhanced Counter Propagation Neural Network for abnormal retinal image classification

Artificial Neural Networks (ANN) is gaining significant importance for pattern recognition applications particularly in the medical field. A hybrid neural network such as Counter Propagation Neural Network (CPN) is highly desirable since it comprises the advantages of supervised and unsupervised training methodologies. Even though it guarantees high accuracy, the network is computationally non-feasible. This drawback is mainly due to the high convergence time period. In this paper, a modified Counter Propagation Neural Network is proposed to tackle this problem which eliminates the iterative training methodology which accounts for the high convergence time. To prove the efficiency, this technique is employed on abnormal retinal image classification system. Real time images from four abnormal classes are used in this work. An extensive feature vector is framed from these images which forms the input for the CPN and the modified CPN. The experimental results of both the networks are analyzed in terms of classification accuracy and convergence time period. The results suggest the superior nature of the proposed technique in terms of convergence time period and classification accuracy.